Year: 2024
Type: conference-paper
Abstract: The Offshore Wind (OSW) industry is experiencing significant expansion, resulting in increased Operations & Maintenance (O&M) costs. Intelligent alarm systems offer the prospect of swift detection of component failures and process anomalies, enabling timely and precise interventions that could yield reductions in resource expenditure, as well as scheduled and unscheduled downtime. This paper introduces an innovative approach to tackle this challenge by capitalising on Large Language Models (LLMs). We present a specialised conversational agent that incorporates statistical techniques to calculate distances between sentences for the detection and filtering of hallucinations and unsafe output. This potentially enables improved interpretation of alarm sequences and the generation of safer repair action recommendations by the agent. Preliminary findings are presented with the approach applied to ChatGPT-4 generated test sentences. The limitation of using ChatGPT-4 and the potential for enhancement of this agent through re-training with specialised OSW datasets are discussed.
Authors: C Walker, C Rothon, K Aslansefat, Y Papadopoulos, N Dethlefs
Institution: University of Hull
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Awards: G299054031, 4 year PhD, including the PG Diploma Offshore Wind Energy and the Environment, G5045220161, 4 year PhD, including the PG Diploma Offshore Wind Energy and the Environment, EPSRC and NERC Centre for Doctoral Training in Offshore Wind Energy and the Environment, G73270079, G8568488323
OpenAlex ID: W4404068686